{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dense\n",
    "from keras.models import Sequential\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import numpy as np\n",
    "import math\n",
    "import efinance as ef\n",
    "from math import sqrt\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "import re\n",
    "\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "# import tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      159501.SZ\n",
      "1      159506.SZ\n",
      "2      159509.SZ\n",
      "3      159513.SZ\n",
      "4      159519.SZ\n",
      "         ...    \n",
      "96     513970.SH\n",
      "97     513980.SH\n",
      "98     513990.SH\n",
      "99     159518.SZ\n",
      "100    513870.SH\n",
      "Name: thscode, Length: 101, dtype: object\n",
      "['159501.SZ', '159506.SZ', '159509.SZ', '159513.SZ', '159519.SZ', '159605.SZ', '159607.SZ', '159612.SZ', '159615.SZ', '159632.SZ', '159636.SZ', '159655.SZ', '159659.SZ', '159660.SZ', '159687.SZ', '159688.SZ', '159691.SZ', '159696.SZ', '159699.SZ', '159711.SZ', '159712.SZ', '159718.SZ', '159726.SZ', '159735.SZ', '159740.SZ', '159741.SZ', '159742.SZ', '159747.SZ', '159750.SZ', '159751.SZ', '159776.SZ', '159788.SZ', '159792.SZ', '159822.SZ', '159823.SZ', '159850.SZ', '159866.SZ', '159892.SZ', '159920.SZ', '159941.SZ', '159954.SZ', '159960.SZ', '510900.SH', '513000.SH', '513010.SH', '513020.SH', '513030.SH', '513040.SH', '513050.SH', '513060.SH', '513070.SH', '513080.SH', '513090.SH', '513100.SH', '513110.SH', '513120.SH', '513130.SH', '513140.SH', '513150.SH', '513160.SH', '513180.SH', '513190.SH', '513200.SH', '513220.SH', '513230.SH', '513260.SH', '513280.SH', '513290.SH', '513300.SH', '513310.SH', '513320.SH', '513330.SH', '513360.SH', '513380.SH', '513390.SH', '513500.SH', '513520.SH', '513530.SH', '513550.SH', '513560.SH', '513580.SH', '513590.SH', '513600.SH', '513650.SH', '513660.SH', '513690.SH', '513700.SH', '513770.SH', '513800.SH', '513810.SH', '513860.SH', '513880.SH', '513890.SH', '513900.SH', '513950.SH', '513960.SH', '513970.SH', '513980.SH', '513990.SH', '159518.SZ', '513870.SH']\n"
     ]
    }
   ],
   "source": [
    "from keras import backend as K\n",
    "data_kua = pd.read_excel(r\"C:\\Users\\Lenovo\\OneDrive\\桌面\\A金融大湾区\\回测数据\\第二题\\跨境etf代码表.xlsx\")\n",
    "code_names = data_kua['thscode']\n",
    "code_data = []\n",
    "print(code_names)\n",
    "for i in code_names:\n",
    "    if i not in code_data:\n",
    "        code_data.append(i)\n",
    "print(code_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_have(name:str)->str:\n",
    "    name = re.findall(\"\\d+\",name)[0]\n",
    "    return name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>布林带</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'code_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\Lenovo\\jupyter_notebook\\Untitled-2.ipynb Cell 5\u001b[0m line \u001b[0;36m1\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/Lenovo/jupyter_notebook/Untitled-2.ipynb#X24sZmlsZQ%3D%3D?line=164'>165</a>\u001b[0m     \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39m--------------------------------------------------\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/Lenovo/jupyter_notebook/Untitled-2.ipynb#X24sZmlsZQ%3D%3D?line=165'>166</a>\u001b[0m     db_all_\u001b[39m.\u001b[39mappend([data_do,xpl,shouyilv,beta])\n\u001b[1;32m--> <a href='vscode-notebook-cell:/c%3A/Users/Lenovo/jupyter_notebook/Untitled-2.ipynb#X24sZmlsZQ%3D%3D?line=166'>167</a>\u001b[0m \u001b[39mfor\u001b[39;00m index \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mlen\u001b[39m(code_data)):\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/Lenovo/jupyter_notebook/Untitled-2.ipynb#X24sZmlsZQ%3D%3D?line=167'>168</a>\u001b[0m     main(index)\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/Lenovo/jupyter_notebook/Untitled-2.ipynb#X24sZmlsZQ%3D%3D?line=168'>169</a>\u001b[0m \u001b[39mprint\u001b[39m(db_all_)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'code_data' is not defined"
     ]
    }
   ],
   "source": [
    "import efinance as ef\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "\n",
    "db_all_ = []\n",
    "\n",
    "neam_ids = ef.futures.get_futures_base_info()\n",
    "hua = 0.01\n",
    "def get_name_index(name):\n",
    "    return neam_ids['行情ID'][neam_ids[neam_ids['期货名称'] == f'{name}'].index.tolist()[0]]\n",
    "\n",
    "def split_to_data(data,name,wheel):\n",
    "    data_wheel = []\n",
    "    for i in range(5,len(data)):\n",
    "        data_wheel.append(np.average(data[name][i-wheel:i-1]))\n",
    "    return data_wheel\n",
    "def find_h_l(data,wheel):\n",
    "    data_h = []\n",
    "    data_l = []\n",
    "    close_h = []\n",
    "    close_l = []\n",
    "    for i in range(5, len(data)):\n",
    "        data_h.append(np.max(data['最高'][i - wheel:i - 1]))\n",
    "        data_l.append(np.min(data['最低'][i - wheel:i - 1]))\n",
    "        close_h.append(np.max(data['收盘'][i - wheel:i - 1]))\n",
    "        close_l.append(np.min(data['收盘'][i - wheel:i - 1]))\n",
    "    return data_h,data_l,close_h,close_l\n",
    "\n",
    "def one_to(data_to_one):\n",
    "    one_to_data = []\n",
    "    pif = np.max(data_to_one) - np.min(data_to_one)\n",
    "    for i in data_to_one:\n",
    "        one_to_data.append((i - np.min(data_to_one))/pif)\n",
    "    return one_to_data\n",
    "\n",
    "def main(index):\n",
    "    \"\"\"\n",
    "    k1 = 0.5\n",
    "    k2 = 0.1\n",
    "    \"\"\"\n",
    "    k1 = 1\n",
    "    k2 = 1\n",
    "    wheel = 5\n",
    "    m_time = 30\n",
    "    id_futures = get_have(code_data[index])\n",
    "    len_data = 0\n",
    "    line_values = 0\n",
    "    data_do = [[-3]]\n",
    "    data_base = ef.stock.get_quote_history(id_futures, klt=5,beg='20231010',end='20231024')\n",
    "    # print(data_base)\n",
    "    if len(data_base) == 0:\n",
    "        return\n",
    "    try:\n",
    "        data_base.to_excel(f\"{str(data_base['日期'][0])[:-6]}.xlsx\")\n",
    "    except:\n",
    "        pass\n",
    "    data_day = np.array(data_base['日期'])\n",
    "    data_base_open = np.array(data_base['开盘'])\n",
    "    data_base_close = np.array(data_base['收盘'])\n",
    "    data_base_zdf = np.array(data_base['涨跌幅'])\n",
    "    benefit = []\n",
    "    for i in range(len(data_base_close)-1):\n",
    "        benefit.append(data_base_close[i+1]/data_base_close[i])\n",
    "    beta = np.cov(benefit)/np.var(benefit)\n",
    "    yuzhi = (data_base_zdf.max()-data_base_zdf.min())/2\n",
    "    # print(data_base_zdf)\n",
    "    # print(data_base.columns)\n",
    "    # print(data_base)\n",
    "    data_h, data_l, close_h, close_l = find_h_l(data_base,wheel)\n",
    "    public_dates = ef.fund.get_public_dates(id_futures)\n",
    "    kk = ef.fund.get_invest_position(id_futures, dates=public_dates[0])\n",
    "    try:\n",
    "        bodonglv = ((np.sum(kk['持仓占比']*kk['较上期变化'],axis=0))/10)\n",
    "    except:\n",
    "        bodonglv = None\n",
    "    guo = 0.02\n",
    "    for i in range(5,len(data_h)+5):\n",
    "        range_k = max(data_h[i-5]-close_l[i-5],close_h[i-5]-data_l[i-5])\n",
    "        buy_line = data_base_open[i] + k1*range_k\n",
    "        sell_line = data_base_open[i] - k2*range_k\n",
    "        if data_do[-1][0]:\n",
    "            pass\n",
    "        if data_base_close[i] > buy_line:\n",
    "            if data_do[-1][0] != '买涨':\n",
    "                # print('买涨',data_base_close[i])\n",
    "                data_do.append(['买涨',data_base_close[i],i])\n",
    "            else:\n",
    "                pass\n",
    "        if data_base_close[i] < sell_line:\n",
    "            if data_do[-1][0] == '买涨':\n",
    "                # print('卖出',data_base_close[i])\n",
    "                data_do.append(['卖出', data_base_close[i],i])\n",
    "            else:\n",
    "                pass\n",
    "        try:\n",
    "            if data_do[-1][0] == '买涨' and data_base_zdf[i] > 0:\n",
    "                # if data_base_zdf[i] > 0.6:\n",
    "                #     data_do.append(['卖出', data_base_close[i], i])\n",
    "                pass\n",
    "            elif data_do[-1][0] == '买涨' and data_base_zdf[i] < 0:\n",
    "                if data_base_zdf[i] < -yuzhi:\n",
    "                    data_do.append(['卖出', data_base_close[i], i])\n",
    "            elif data_do[-1][0] != '买涨' and data_base_zdf[i] < 0:\n",
    "                # if data_base_zdf[i] < -0.6:\n",
    "                #     data_do.append(['买涨', data_base_close[i], i])\n",
    "                pass\n",
    "            elif data_do[-1][0] != '买涨' and data_base_zdf[i] > 0:\n",
    "                if data_base_zdf[i] > yuzhi:\n",
    "                    data_do.append(['买涨', data_base_close[i], i])\n",
    "        except:\n",
    "            pass\n",
    "    data_do_frame = pd.DataFrame(data_do[1:],columns=['方式','收盘价','时间'])\n",
    "    # plt.subplot(2,1,1)\n",
    "    # plt.plot(range(len(data_base_close)),data_base_close)\n",
    "    win_number = 0\n",
    "    # for x,y,tp in zip(data_do_frame['时间'],data_do_frame['收盘价'],data_do_frame['方式']):\n",
    "    #     if tp == '买涨':\n",
    "    #         plt.scatter(x,y,c = 'r')\n",
    "    #     if tp == '卖出':\n",
    "    #         plt.scatter(x,y,c = 'g')\n",
    "\n",
    "    # plt.subplot(2, 1, 2)\n",
    "    # plt.plot(range(len(data_base['涨跌幅'])),one_to(data_base['涨跌幅']))\n",
    "    # plt.show()\n",
    "    win_arr = []\n",
    "    lose_arr = []\n",
    "    data_do = data_do[1:]\n",
    "    for i in range(len(data_do)-1):\n",
    "        if data_do[i][0] == '买涨':\n",
    "            income = (data_do[i + 1][1]*(1-hua) - data_do[i][1])/data_do[i][1]\n",
    "            if income >= 0:\n",
    "                win_arr.append(income)\n",
    "            else:\n",
    "                lose_arr.append(income)\n",
    "        else:\n",
    "            income = (data_do[i][1] - data_do[i + 1][1]*(1-hua))/data_do[i][1]\n",
    "            if income >= 0:\n",
    "                win_arr.append(income)\n",
    "            else:\n",
    "                lose_arr.append(income)\n",
    "    try:\n",
    "        shouyilv = np.sum(win_arr)\n",
    "        max_lose = np.min(lose_arr)\n",
    "    except:\n",
    "        shouyilv = None\n",
    "        max_lose = None\n",
    "    print('收益率',shouyilv)\n",
    "    print('最大回撤',max_lose)\n",
    "    print('Beta',beta)\n",
    "    for i in data_do:\n",
    "        i[2] = data_day[i[2]]\n",
    "    # print(pd.DataFrame(data_do,columns=['行为','买入价','日期']))\n",
    "    # win_arr.extend(lose_arr)\n",
    "    try:\n",
    "        xpl = (shouyilv - guo)/bodonglv\n",
    "    except:\n",
    "        xpl = None\n",
    "    print('夏普率:',xpl)\n",
    "    print(f'k1:{k1},k2:{k2}')\n",
    "    print('--------------------------------------------------')\n",
    "    db_all_.append([data_do,xpl,shouyilv,beta])\n",
    "for index in range(len(code_data)):\n",
    "    main(index)\n",
    "print(db_all_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "db_fram = pd.DataFrame(db_all_,columns = ['行为','夏普率','收益率','Beta'])\n",
    "db_fram = db_fram[['夏普率','收益率','Beta']]\n",
    "db_fram = pd.concat([db_fram,code_names],axis=1)\n",
    "db_fram.to_excel(r\"c:\\Users\\Lenovo\\Desktop\\第二题.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>LSTM</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def difference(data_set,interval=1):\n",
    "    diff=list()\n",
    "    for i in range(interval,len(data_set)):\n",
    "        value=data_set[i]-data_set[i-interval]\n",
    "        diff.append(value)\n",
    "    return pd.Series(diff)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def timeseries_to_supervised(data,lag=1):\n",
    "    df=pd.DataFrame(data)\n",
    "    columns=[df.shift(1)]\n",
    "    columns.append(df)\n",
    "    df=pd.concat(columns,axis=1)\n",
    "    df.fillna(0,inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scale(train,test):\n",
    "    # 创建一个缩放器，将数据集中的数据缩放到[-1,1]的取值范围中\n",
    "    scaler=MinMaxScaler(feature_range=(-1,1))\n",
    "    # 使用数据来训练缩放器\n",
    "    scaler=scaler.fit(train)\n",
    "    # 使用缩放器来将训练集和测试集进行缩放\n",
    "    train_scaled=scaler.transform(train)\n",
    "    test_scaled=scaler.transform(test)\n",
    "    return scaler,train_scaled,test_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_lstm(train,batch_size,nb_epoch,neurons):\n",
    "    # 将数据对中的x和y分开\n",
    "    X,y=train[:,0:-1],train[:,-1]\n",
    "    # 将2D数据拼接成3D数据，形状为[N*1*1]\n",
    "    X=X.reshape(X.shape[0],1,X.shape[1])\n",
    " \n",
    "    model=Sequential()\n",
    "    model.add(LSTM(neurons,batch_input_shape=(batch_size,X.shape[1],X.shape[2]),stateful=True))\n",
    "    model.add(Dense(1))\n",
    " \n",
    "    model.compile(loss='mean_squared_error',optimizer='adam')\n",
    "    for i in range(nb_epoch):\n",
    "        # shuffle是不混淆数据顺序\n",
    "        his=model.fit(X,y,batch_size=batch_size,verbose=1,shuffle=False)\n",
    "        # 每训练完一次就重置一次网络状态，网络状态与网络权重不同\n",
    "        model.reset_states()\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def forecast_lstm(model,batch_size,X):\n",
    "    # 将形状为[1:]的，包含一个元素的一维数组X，转换形状为[1,1,1]的3D张量\n",
    "    X = X.astype('float64')\n",
    "    X=X[0].reshape(1,1,len(X))\n",
    "    # 输出形状为1行一列的二维数组yhat\n",
    "    yhat=model.predict(X,batch_size=batch_size)\n",
    "    # 将yhat中的结果返回\n",
    "    return yhat[0,0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对预测的数据进行逆差分转换\n",
    "def invert_difference(history,yhat,interval=1):\n",
    "    return yhat+history[-interval]\n",
    " \n",
    "# 将预测值进行逆缩放，使用之前训练好的缩放器，x为一维数组，y为实数\n",
    "def invert_scale(scaler,X,y):\n",
    "    # 将X,y转换为一个list列表\n",
    "    new_row=[x for x in X]+[y]\n",
    "    # 将列表转换为数组\n",
    "    array=np.array(new_row,dtype=object)\n",
    "    # 将数组重构成一个形状为[1,2]的二维数组->[[10,12]]\n",
    "    array=array.reshape(1,len(array))\n",
    "    # 逆缩放输入的形状为[1,2]，输出形状也是如此\n",
    "    invert=scaler.inverse_transform(array)\n",
    "    # 只需要返回y值即可\n",
    "    return invert[0,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.526\n",
       "1       0.526\n",
       "2       0.525\n",
       "3       0.526\n",
       "4       0.528\n",
       "        ...  \n",
       "1518    0.541\n",
       "1519    0.540\n",
       "1520    0.540\n",
       "1521    0.540\n",
       "1522    0.540\n",
       "Name: 收盘, Length: 1523, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import efinance as ef\n",
    "close_data = ef.stock.get_quote_history('513130',klt=5)['收盘']\n",
    "close_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0                          [0.0]\n",
      "1                          [0.0]\n",
      "2         [0.001000000000000112]\n",
      "3        [-0.001000000000000112]\n",
      "4         [0.001000000000000112]\n",
      "                  ...           \n",
      "1518     [0.0009999999999998899]\n",
      "1519    [-0.0009999999999998899]\n",
      "1520                       [0.0]\n",
      "1521                       [0.0]\n",
      "1522                       [0.0]\n",
      "Length: 1523, dtype: object\n",
      "[[0 array([0.])]\n",
      " [array([0.]) array([0.])]\n",
      " [array([0.]) array([0.001])]\n",
      " ...\n",
      " [array([-0.001]) array([0.])]\n",
      " [array([0.]) array([0.])]\n",
      " [array([0.]) array([0.])]]\n",
      "1123/1123 [==============================] - 4s 1ms/step - loss: 0.0108\n",
      "1/1 [==============================] - 1s 562ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_absolute_error: 0.0027471449052915388\n",
      "mean_squared_error: 9.77167170397405e-06\n",
      "rmse: 0.0031259673229216663\n",
      "r2 score: 0.9747140113356714\n"
     ]
    }
   ],
   "source": [
    "from keras import backend as keras_export\n",
    "for k in range(len(code_data)):\n",
    "    code_fund = get_have(code_data[k])\n",
    "    close_data = ef.stock.get_quote_history(code_fund,klt=5)\n",
    "    # close_data.to_excel(rf'c:\\Users\\Lenovo\\Desktop\\跨境etf\\{close_data[\"股票名称\"][0]}.xlsx')\n",
    "    close_data = close_data[['日期','收盘']]\n",
    "    series = close_data.set_index(['日期'],drop=True)\n",
    "    plt.figure(figsize=(10,6))\n",
    "    series['收盘'].plot()\n",
    "    plt.show()\n",
    "    raw_value=series.values\n",
    "    diff_value=difference(raw_value,1)\n",
    "    print(diff_value)\n",
    "    supervised=timeseries_to_supervised(diff_value,1)\n",
    "    supervised_value=supervised.values\n",
    "    print(supervised_value)\n",
    "    testNum=400\n",
    "    train,test=supervised_value[:-testNum],supervised_value[-testNum:]\n",
    "    scaler,train_scaled,test_scaled=scale(train,test)\n",
    "    X,y=train[:,0:-1],train[:,-1]\n",
    "    X=X.reshape(X.shape[0],1,X.shape[1])\n",
    "    # 构建一个LSTM模型并训练，样本数为1，训练次数为3，LSTM层神经元个数为4\n",
    "    lstm_model=fit_lstm(train_scaled,1,1,1)\n",
    "    predictions=list()\n",
    "    for i in range(len(test_scaled)):\n",
    "        # 将测试集拆分为X和y\n",
    "        X,y=test[i,0:-1],test[i,-1]\n",
    "        # 将训练好的模型、测试数据传入预测函数中\n",
    "        yhat=forecast_lstm(lstm_model,1,X)\n",
    "        # 将预测值进行逆缩放\n",
    "        yhat=invert_scale(scaler,X,yhat)\n",
    "        # 对预测的y值进行逆差分\n",
    "        yhat=invert_difference(raw_value,yhat,len(test_scaled)+1-i)\n",
    "        # 存储正在预测的y值\n",
    "        predictions.append(yhat)\n",
    "    plt.plot(raw_value[-testNum:])\n",
    "    plt.plot(predictions)\n",
    "    plt.legend(['true','pred'])\n",
    "    plt.show()\n",
    "    y_test = raw_value[-testNum:]\n",
    "    y_predict = predictions\n",
    "    print(\"mean_absolute_error:\", mean_absolute_error(y_test, y_predict))\n",
    "    print(\"mean_squared_error:\", mean_squared_error(y_test, y_predict))\n",
    "    print(\"rmse:\", sqrt(mean_squared_error(y_test, y_predict)))\n",
    "    print(\"r2 score:\", r2_score(y_test, y_predict))\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.544 0.545 0.545 0.546 0.547 0.547 0.546 0.546 0.546 0.545 0.545 0.546\n",
      " 0.545 0.546 0.545 0.545 0.544 0.544 0.542 0.543 0.544 0.544 0.544 0.544\n",
      " 0.543 0.542 0.542 0.542 0.542 0.542 0.542 0.542 0.541 0.542 0.541 0.542\n",
      " 0.541 0.54  0.541 0.541 0.541 0.54  0.541 0.541 0.542 0.541 0.541 0.541\n",
      " 0.541 0.541 0.541 0.541 0.541 0.54  0.541 0.542 0.541 0.541 0.541 0.541\n",
      " 0.54  0.54  0.54  0.539 0.54  0.54  0.54  0.54  0.539 0.54  0.54  0.54\n",
      " 0.54  0.54  0.54  0.54  0.541 0.541 0.541 0.541 0.542 0.542 0.542 0.542\n",
      " 0.543 0.542 0.542 0.542 0.542 0.542 0.542 0.542 0.542 0.542 0.541 0.541\n",
      " 0.54  0.54  0.54  0.54  0.54  0.54  0.54  0.54  0.54  0.541 0.541 0.541\n",
      " 0.541 0.54  0.54  0.539 0.541 0.541 0.541 0.541 0.541 0.54  0.54  0.541\n",
      " 0.541 0.541 0.541 0.541 0.541 0.541 0.541 0.54  0.541 0.541 0.541 0.541\n",
      " 0.54  0.541 0.541 0.541 0.54  0.541 0.541 0.541 0.541 0.541 0.541 0.54\n",
      " 0.54  0.541 0.541 0.54  0.541 0.54  0.54  0.54  0.541 0.54  0.541 0.541\n",
      " 0.54  0.54  0.539 0.54  0.54  0.54  0.54  0.54  0.54  0.54  0.539 0.54\n",
      " 0.54  0.54  0.54  0.54  0.54 ] [0.545 0.545 0.546 0.547 0.547 0.546 0.546 0.546 0.545 0.545 0.546 0.545\n",
      " 0.546 0.545 0.545 0.544 0.544 0.542 0.543 0.544 0.544 0.544 0.544 0.543\n",
      " 0.542 0.542 0.542 0.542 0.542 0.542 0.542 0.541 0.542 0.541 0.542 0.541\n",
      " 0.54  0.541 0.541 0.541 0.54  0.541 0.541 0.542 0.541 0.541 0.541 0.541\n",
      " 0.541 0.541 0.541 0.541 0.54  0.541 0.542 0.541 0.541 0.541 0.541 0.54\n",
      " 0.54  0.54  0.539 0.54  0.54  0.54  0.54  0.539 0.54  0.54  0.54  0.54\n",
      " 0.54  0.54  0.54  0.541 0.541 0.541 0.541 0.542 0.542 0.542 0.542 0.543\n",
      " 0.542 0.542 0.542 0.542 0.542 0.542 0.542 0.542 0.542 0.541 0.541 0.54\n",
      " 0.54  0.54  0.54  0.54  0.54  0.54  0.54  0.54  0.541 0.541 0.541 0.541\n",
      " 0.54  0.54  0.539 0.541 0.541 0.541 0.541 0.541 0.54  0.54  0.541 0.541\n",
      " 0.541 0.541 0.541 0.541 0.541 0.541 0.54  0.541 0.541 0.541 0.541 0.54\n",
      " 0.541 0.541 0.541 0.54  0.541 0.541 0.541 0.541 0.541 0.541 0.54  0.54\n",
      " 0.541 0.541 0.54  0.541 0.54  0.54  0.54  0.541 0.54  0.541 0.541 0.54\n",
      " 0.54  0.539 0.54  0.54  0.54  0.54  0.54  0.54  0.54  0.539 0.54  0.54\n",
      " 0.54  0.54  0.54  0.54  0.54 ]\n",
      "Epoch 1/10\n",
      "170/170 - 2s - loss: 0.0600 - 2s/epoch - 10ms/step\n",
      "Epoch 2/10\n",
      "170/170 - 0s - loss: 9.1177e-07 - 242ms/epoch - 1ms/step\n",
      "Epoch 3/10\n",
      "170/170 - 0s - loss: 8.7010e-07 - 194ms/epoch - 1ms/step\n",
      "Epoch 4/10\n",
      "170/170 - 0s - loss: 9.3131e-07 - 199ms/epoch - 1ms/step\n",
      "Epoch 5/10\n",
      "170/170 - 0s - loss: 9.3700e-07 - 197ms/epoch - 1ms/step\n",
      "Epoch 6/10\n",
      "170/170 - 0s - loss: 9.5975e-07 - 201ms/epoch - 1ms/step\n",
      "Epoch 7/10\n",
      "170/170 - 0s - loss: 9.8846e-07 - 195ms/epoch - 1ms/step\n",
      "Epoch 8/10\n",
      "170/170 - 0s - loss: 9.6475e-07 - 198ms/epoch - 1ms/step\n",
      "Epoch 9/10\n",
      "170/170 - 0s - loss: 1.1223e-06 - 194ms/epoch - 1ms/step\n",
      "Epoch 10/10\n",
      "170/170 - 0s - loss: 1.0583e-06 - 200ms/epoch - 1ms/step\n",
      "1/1 [==============================] - 0s 250ms/step\n",
      "预测结果: [0.541      0.54       0.54       0.541      0.541      0.54\n",
      " 0.541      0.54       0.541      0.541      0.541      0.541\n",
      " 0.541      0.541      0.541      0.541      0.541      0.541\n",
      " 0.541      0.541      0.542      0.542      0.542      0.542\n",
      " 0.542      0.541      0.542      0.542      0.542      0.541\n",
      " 0.541      0.541      0.541      0.541      0.541      0.541\n",
      " 0.541      0.542      0.542      0.542      0.54202181]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, Dense\n",
    "import numpy as np\n",
    "import efinance as ef\n",
    "# 示例数据\n",
    "x = np.array(ef.stock.get_quote_history('513130',klt=1)['收盘'])[:-40]\n",
    "y = x[1:]\n",
    "x = x[:-1]\n",
    "print(x,y)\n",
    "\n",
    "# 将数据转换为 LSTM 模型的输入格式\n",
    "def create_lstm_dataset(data, look_back=1):\n",
    "    X, y = [], []\n",
    "    for i in range(len(data) - look_back):\n",
    "        X.append(data[i:(i + look_back)])\n",
    "        y.append(data[i + look_back])\n",
    "    return np.array(X), np.array(y)\n",
    "\n",
    "look_back = 3\n",
    "X, y = create_lstm_dataset(x, look_back)\n",
    "\n",
    "# 转换为 LSTM 模型需要的输入格式 (samples, time steps, features)\n",
    "X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n",
    "\n",
    "# 定义 LSTM 模型\n",
    "model = Sequential()\n",
    "model.add(LSTM(units=50, activation='relu', input_shape=(look_back, 1)))\n",
    "model.add(Dense(units=1))  # 输出层\n",
    "\n",
    "# 编译模型\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# 训练模型\n",
    "model.fit(X, y, epochs=10, batch_size=1, verbose=2)\n",
    "\n",
    "# 预测未来的多个时间步\n",
    "future_data = np.array(ef.stock.get_quote_history('513130', klt=1)['收盘'])[-40:]\n",
    "\n",
    "# 单步预测\n",
    "predicted_value = model.predict(np.reshape(future_data[-look_back:], (1, look_back, 1)))\n",
    "\n",
    "# 将预测值添加到 future_data 中\n",
    "future_data = np.append(future_data, predicted_value)\n",
    "\n",
    "print(\"预测结果:\", future_data)\n",
    "\n",
    "# 绘制真实值和预测值\n",
    "plt.plot(range(len(np.array(ef.stock.get_quote_history('513130', klt=1)['收盘'])[-40:])), np.array(ef.stock.get_quote_history('513130', klt=1)['收盘'])[-40:])\n",
    "plt.plot(range(len(future_data)), future_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测结果: [0.541      0.54       0.54       0.541      0.541      0.54\n",
      " 0.541      0.54       0.541      0.541      0.541      0.541\n",
      " 0.541      0.541      0.541      0.541      0.541      0.541\n",
      " 0.541      0.541      0.542      0.542      0.542      0.542\n",
      " 0.542      0.541      0.542      0.542      0.542      0.541\n",
      " 0.541      0.541      0.541      0.541      0.541      0.541\n",
      " 0.541      0.542      0.542      0.542      0.54202181]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"预测结果:\", future_data)\n",
    "# print(ef.stock.get_quote_history('513130',klt=1)[-40:])\n",
    "# print(len(future_data))\n",
    "# print(len(np.array(ef.stock.get_quote_history('513130',klt=1)['收盘'])[-40:]))\n",
    "plt.plot(range(len(np.array(ef.stock.get_quote_history('513130',klt=1)['收盘'])[-40:])),np.array(ef.stock.get_quote_history('513130',klt=1)['收盘'])[-40:])\n",
    "plt.plot(range(len(future_data)),future_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>MLP</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.061 1.06  1.062]\n",
      " [1.06  1.062 1.061]\n",
      " [1.062 1.061 1.061]\n",
      " ...\n",
      " [0.983 0.983 0.983]\n",
      " [0.983 0.983 0.983]\n",
      " [0.983 0.983 0.983]]\n",
      "[1.061 1.061 1.062 ... 0.983 0.983 0.983]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2ac93b6a710>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy import array\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "\n",
    "# 构造监督学习型数据\n",
    "def split_sequence(sequence, n_steps):\n",
    "\tX, y = list(), list()\n",
    "\tfor i in range(len(sequence)):\n",
    "\t\t# 获取待预测数据的位置\n",
    "\t\tend_ix = i + n_steps\n",
    "\t\t# 如果待预测数据超过序列长度，构造完成\n",
    "\t\tif end_ix >= len(sequence):\n",
    "\t\t\tbreak\n",
    "\t\t# 分别汇总 输入 和 输出 数据集\n",
    "\t\tseq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "\t\tX.append(seq_x)\n",
    "\t\ty.append(seq_y)\n",
    "\treturn array(X), array(y)\n",
    "\n",
    "# define input sequence\n",
    "code_fund = get_have(code_data[0])\n",
    "raw_seq = ef.stock.get_quote_history(code_fund,klt=5)['收盘']\n",
    "# 定义时间步，即用几个数据作为输入，预测另一个数据\n",
    "n_steps = 3\n",
    "# 构造监督学习型数据\n",
    "X, y = split_sequence(raw_seq[:-150], n_steps)\n",
    "print(X)\n",
    "print(y)\n",
    "# 定义一个简单的MLP模型\n",
    "model = Sequential()\n",
    "model.add(Dense(100, activation='relu', input_dim=n_steps))\n",
    "model.add(Dense(1))\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "# fit model\n",
    "model.fit(X, y, epochs=1000, verbose=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.055523682\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_absolute_error: 0.01654367583274839\n",
      "mean_squared_error: 0.000470758661104375\n",
      "rmse: 0.021696973547118848\n",
      "r2 score: -130.21826878815284\n"
     ]
    }
   ],
   "source": [
    "# 手动定义一个输入\n",
    "x_input = array(raw_seq[-150:])\n",
    "dd = len(x_input)//n_steps\n",
    "# 将(3,)的序列[70, 80, 90]，重构成(1, 3)的序列[[70, 80, 90]]\n",
    "x_input = x_input.reshape((dd, n_steps))\n",
    "yhat = model.predict(x_input, verbose=0)\n",
    "\n",
    "sum = []\n",
    "for i,j in zip(yhat,raw_seq):\n",
    "    sum.append(i-j)\n",
    "print(np.average(sum))\n",
    "plt.plot(range(len(yhat)),yhat)\n",
    "plt.plot(range(len(raw_seq[int(-150/n_steps):])),raw_seq[int(-150/n_steps):])\n",
    "plt.show()\n",
    "\n",
    "y_test = raw_seq[int(-150/n_steps):]\n",
    "y_predict = yhat\n",
    "print(\"mean_absolute_error:\", mean_absolute_error(y_test, y_predict))\n",
    "print(\"mean_squared_error:\", mean_squared_error(y_test, y_predict))\n",
    "print(\"rmse:\", sqrt(mean_squared_error(y_test, y_predict)))\n",
    "print(\"r2 score:\", r2_score(y_test, y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
